We profiled the secretomes of 6 NSCLC cell lines with varying IC50-values for cisplatin, using label-free GeLC-MS/MS-based proteomics. Out of a total dataset of 2618 proteins, 304 proteins showed significant differences in expression levels between cisplatin sensitive and insensitive cell lines. Functional data mining revealed that the secretion of typically extracellular factors was associated with a higher sensitivity towards cisplatin, while cisplatin insensitivity correlated with increased secretion of theoretically intra-cellular proteins. Stringent statistical analysis and quantitative filtering yielded 58 biomarker candidates, 34 of which could be detected in clinical biofluids of lung cancer patients such as sputum using label-free LC-MS/MS-based proteomics. To assess performance of these biofluid biomarker candidates, we correlated protein expression with patient survival using a publically available clinical gene expression data set (GSE14814). We thus identified 3 top candidates with potential predictive value in determining cisplatin response (UGGT1, COL6A1 and MAP4) for future development as non-invasive biomarkers to guide treatment decisions.